Year: 2026
Type: conference-paper
Abstract: Large Language Models (LLMs) show promise for automated code repair but often struggle with the complex semantic and structural correctness required.We present SYNTHFIX, a hybrid neuro-symbolic framework that improves LLM-based vulnerability repair by unifying code synthesis with compiler-informed symbolic feedback.The core of our approach is an adaptive training strategy where a neural Router Model directs code samples to either Supervised Fine-Tuning (SFT) to learn common patterns or Reward Fine-Tuning (RFT) with symbolic rewards for complex, iterative refinement.On the FixJS (JavaScript) and Code-Flaws (C) benchmarks, SYNTHFIX achieves up to 18% relative improvement in Code-BLEU/CrystalBLEU and 32% in Exact Match over strong SFT and RFT baselines.Our results show that this adaptive combination of training strategies, which mirrors how developers alternate between pattern application and tool feedback, significantly improves the accuracy and efficiency of LLM-based vulnerability repair.Our code and data are available at https: //github.com/CoderDoge1108/SynthFix 1 .
Authors: Yifan Zhang, Jieyu Li, Kexin Pei, Yu Huang, Kevin Leach
Institutions: Vanderbilt University, University of Chicago
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166822202